{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import optimize as opt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def getalldata():  # 获取股票信息\n",
    "\n",
    "    Stock = pd.read_csv(\"fianl_data.csv\")\n",
    "    return Stock\n",
    "\n",
    "\n",
    "def lingotest():\n",
    "    # 确定c,A,b,Aeq,beq\n",
    "    c = np.array([40, 90])  # 目标函数变量系数\n",
    "    A = np.array([[9, 7], [7, 20]])  # 不等式变量系数\n",
    "    b = np.array([56, 70])  # 不等式变量值\n",
    "    Aeq = np.array([[0, 0]])  # 等式变量系数\n",
    "    beq = np.array([0])  # 等式变量值\n",
    "    # 限制\n",
    "    lim1 = (0, None)\n",
    "    lim2 = (0, None)\n",
    "    # 求解\n",
    "    res = opt.linprog(-c, A, b, Aeq, beq, bounds=(lim1, lim2))\n",
    "    # 输出结果\n",
    "    return res\n",
    "\n",
    "\n",
    "def lingo_solve1_fundment(y1, y2, p1, p2, x10, x20, x30, beta=0.99, wiga=0.1, c1=0.01, c2=0.02):\n",
    "    '''\n",
    "    from Mon to Thu fundemential\n",
    "\n",
    "    @:arg unknow 未知量 x1 x2 x3  alpha ,z\n",
    "    @:parameter  设定系数 beta = 0.95 wiga = 0.1\n",
    "    @:parameter known\n",
    "    已知量\n",
    "    Y1,Y2 预测价格\n",
    "    p1,p2 初始价格\n",
    "    C1，C2 = 手续费率\n",
    "    :return:  未知量 x1 x2 x3\n",
    "    '''\n",
    "    # 确定c,A,b,Aeq,beq\n",
    "    # when x1>x10 x2>x20\n",
    "    c = np.array([-y1 / p1 + c1, -y2 / p2 + c2, -1, 0, 0])  # 目标函数变量系数\n",
    "    A = np.array([[0, 0, 0, 1, 1 / (1 - beta)], [-y1 / p1, -y2 / p2, -1, -1, -1]])  # 不等式变量系数<=\n",
    "    b = np.array([wiga, -x10 - x20 - x30])  # 不等式变量值\n",
    "    Aeq = np.array([[1 + c1, 1 + c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 + c1) * x10 + (1 + c2) * x20 + x30])  # 等式变量值\n",
    "    # 限制\n",
    "    lim1 = (x10, None)\n",
    "    lim2 = (x20, None)\n",
    "    lim3 = (0, None)\n",
    "    lim4 = (0, None)\n",
    "    lim5 = (None, None)\n",
    "    # 求解# min\n",
    "    res = opt.linprog(-c, A, b, Aeq, beq, bounds=(lim1, lim2, lim3, lim4, lim5))\n",
    "    return res\n",
    "\n",
    "\n",
    "def lingo_solve1(y1, y2, p1, p2, x10, x20, x30,UR=0.75,beta=0.9, wiga=0.1, c1=0.01, c2=0.02):\n",
    "    '''\n",
    "    from Mon to Thu fundemential\n",
    "\n",
    "    @:arg unknow 未知量 x1 x2 x3  alpha ,z\n",
    "    @:parameter  设定系数 beta = 0.95 wiga = 0.1\n",
    "    @:parameter known\n",
    "    已知量\n",
    "    Y1,Y2 预测价格\n",
    "    p1,p2 初始价格\n",
    "    C1，C2 = 手续费率\n",
    "    :return:  未知量 x1 x2 x3\n",
    "\n",
    "    '''\n",
    "    # 确定c,A,b,Aeq,beq\n",
    "    ##################### when x1>x10 x2>x20######################\n",
    "    fun_min = 9999999\n",
    "    final_solve = None\n",
    "    c = np.array([-y1 / p1 + c1, -y2 / p2 + c2, -1, 0, 0])  # 目标函数变量系数\n",
    "    A = np.array([[0, 0, 0, 1, 1 / (1 - beta)], [-y1 / p1, -y2 / p2, -1, -1, -1],[1,0,0,0,0],[0,1,0,0,0]])  # 不等式变量系数<=\n",
    "    b = np.array([wiga, -x10 - x20 - x30,UR*(x10+x20+x30),UR*(x10+x20+x30)])  # 不等式变量值\n",
    "    Aeq = np.array([[1 + c1, 1 + c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 + c1) * x10 + (1 + c2) * x20 + x30])  # 等式变量值\n",
    "    # 限制\n",
    "    lim1 = (x10, None)\n",
    "    lim2 = (x20, None)\n",
    "    lim3 = (0, None)\n",
    "    lim4 = (None,None )\n",
    "    lim5 = (0, None)\n",
    "    # 求解# min\n",
    "    res = opt.linprog(c, A, b, Aeq, beq, bounds=(lim1, lim2, lim3, lim4, lim5))\n",
    "    solve_success = res.get('success')\n",
    "    #print(res)\n",
    "    if solve_success and res.get('fun') < fun_min:\n",
    "        fun_min = min(res.get('fun'), fun_min)\n",
    "        final_solve = res.get('x')\n",
    "        # print(fun_min)\n",
    "        # print(final_solve)\n",
    "        # print(1)\n",
    "\n",
    "\n",
    "\n",
    "    ################# when x1 > x10, x2 < x20#######################\n",
    "    c = np.array([-y1 / p1 + c1, -y2 / p2 - c2, -1, 0, 0])\n",
    "    Aeq = np.array([[1 + c1, 1 - c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 + c1) * x10 + (1 - c2) * x20 + x30])  # 等式变量值\n",
    "    lim1 = (x10, None)\n",
    "    lim2 = (0, x20)\n",
    "\n",
    "    res = opt.linprog(c, A, b, Aeq, beq, bounds=(lim1, lim2, lim3, lim4, lim5))\n",
    "    #print(res)\n",
    "    solve_success = res.get('success')\n",
    "\n",
    "    if solve_success and res.get('fun') < fun_min:\n",
    "        fun_min = res.get('fun')\n",
    "        final_solve = res.get('x')\n",
    "        # print(fun_min)\n",
    "        # print(final_solve)\n",
    "        # print(1)\n",
    "    ################# when x1 < x10, x2 > x20#####################\n",
    "\n",
    "    c = np.array([-y1 / p1 - c1, -y2 / p2 + c2, -1, 0, 0])\n",
    "    Aeq = np.array([[1 - c1, 1 + c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 - c1) * x10 + (1 + c2) * x20 + x30])  # 等式变量值\n",
    "    lim1 = (0, x10)\n",
    "    lim2 = (x20, None)\n",
    "\n",
    "    res = opt.linprog(c, A, b, Aeq, beq, bounds=(lim1, lim2, lim3, lim4, lim5))\n",
    "    #print(res)\n",
    "    solve_success = res.get('success')\n",
    "    if solve_success and res.get('fun') < fun_min:\n",
    "        fun_min = res.get('fun')\n",
    "        final_solve = res.get('x')\n",
    "        # print(fun_min)\n",
    "        # print(final_solve)\n",
    "        # print(1)\n",
    "    # 输出结果\n",
    "\n",
    "    ################# when x1 < x10, x2 < x20#####################\n",
    "\n",
    "    c = np.array([-y1 / p1 - c1, -y2 / p2 - c2, -1, 0, 0])\n",
    "    Aeq = np.array([[1 - c1, 1 - c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 - c1) * x10 + (1 - c2) * x20 + x30])  # 等式变量值\n",
    "    lim1 = (0, x10)\n",
    "    lim2 = (0, x20)\n",
    "\n",
    "    res = opt.linprog(c, A, b, Aeq, beq, bounds=(lim1, lim2, lim3, lim4, lim5))\n",
    "    #print(res)\n",
    "    solve_success = res.get('success')\n",
    "    if solve_success and res.get('fun') < fun_min:\n",
    "        fun_min = res.get('fun')\n",
    "        final_solve = res.get('x')\n",
    "        # print(fun_min)\n",
    "        # print(final_solve)\n",
    "        # print(1)\n",
    "    # 输出结果\n",
    "    #print(final_solve[4])\n",
    "    return [final_solve,res]\n",
    "\n",
    "\n",
    "def lingo_solve2(y2, p2, x20, x30, beta=0.9, wiga=1000, c2=0.02):\n",
    "    '''\n",
    "        from Mon to Thu fundemential\n",
    "\n",
    "        @:arg unknow 未知量 x2 x3  alpha ,z\n",
    "        @:parameter  设定系数 beta = 0.95 wiga = 0.1\n",
    "        @:parameter known\n",
    "        已知量\n",
    "        Y2 预测价格\n",
    "        p2 初始价格\n",
    "        C2 = 手续费率\n",
    "        x20 x30 之前的财产\n",
    "        :return:  移动后的 x2 x3\n",
    "    '''\n",
    "    # 确定c,A,b,Aeq,beq\n",
    "    ######### when x2>x20#########\n",
    "    fun_min = 9999999\n",
    "    final_solve = None\n",
    "    c = np.array([-y2 / p2 + c2, -1, 0, 0])  # 目标函数变量系数\n",
    "    A = np.array([[0, 0, 1, 1 / (1 - beta)], [-y2 / p2, -1, -1, -1]])  # 不等式变量系数<=\n",
    "    b = np.array([wiga, - x20 - x30])  # 不等式变量值\n",
    "    Aeq = np.array([[1 + c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 + c2) * x20 + x30])  # 等式变量值\n",
    "    # 限制\n",
    "    lim2 = (x20, None)\n",
    "    lim3 = (0, None)\n",
    "    lim4 = (None,None )\n",
    "    lim5 = (0, None)\n",
    "    # 求解# min\n",
    "    res = opt.linprog(c, A, b, Aeq, beq, bounds=(lim2, lim3, lim4, lim5))\n",
    "    #print(res)\n",
    "    solve_success = res.get('success')\n",
    "    if solve_success and res.get('fun') < fun_min:\n",
    "        fun_min = res.get('fun')\n",
    "        final_solve = res.get('x')\n",
    "        #print(final_solve)\n",
    "\n",
    "    ######### when x2<=x20\n",
    "    c = np.array([-y2 / p2 - c2, -1, 0, 0])  # 目标函数变量系数\n",
    "    Aeq = np.array([[1 - c2, 1, 0, 0]])  # 等式变量系数\n",
    "    beq = np.array([(1 - c2) * x20 + x30])  # 等式变量值\n",
    "    # 输出结果\n",
    "    lim2 = (0, x20)\n",
    "    res = opt.linprog(c, A, b, Aeq, beq, bounds=(lim2, lim3, lim4, lim5))\n",
    "    solve_success = res.get('success')\n",
    "   \n",
    "    if solve_success and res.get('fun') < fun_min:\n",
    "        fun_min = res.get('fun')\n",
    "        final_solve = res.get('x')\n",
    "        #print(final_solve)\n",
    "    #print(res)\n",
    "    return final_solve#,res\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'proces' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_1504/2558329649.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mproces\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'proces' is not defined"
     ]
    }
   ],
   "source": [
    "proces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "gold = pd.Series(Stock['gold_pre'])\n",
    "glod_real = pd.Series(Stock['USD (PM)'])\n",
    "bit = np.array(Stock['predict'])\n",
    "bit_real = np.array(Stock['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_1504/3226142770.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;31m# solve\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[0mprocess_temp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlingo_solve1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgold\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbit\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mglod_real\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbit_real\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m         \u001b[0mprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess_temp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     46\u001b[0m         \u001b[0mprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess_temp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[0mprocess\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess_temp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "day = len(gold)\n",
    "process = np.zeros([day,3])\n",
    "process[58,0]= 0\n",
    "process[58,1]= 0\n",
    "process[58,2]= 1000\n",
    "for i in range(59,day-1):\n",
    "    if pd.isna(glod_real[i]):\n",
    "        #print(i,\" is non \")\n",
    "        process[i,0] = process[i-1,0]* bit_real[i]/bit_real[i-1]\n",
    "        process[i,1] = process[i-1,1] \n",
    "        process[i,2] = process[i-1,2]\n",
    "        process_temp = lingo_solve2(bit[i+1],bit_real[i],process[i,1],process[i,2]); # solve the tomorrow's value ,only the bitchain and the money\n",
    "        process[i,1] = process_temp[0]\n",
    "        process[i,2] = process_temp[1]\n",
    "        #print(process_temp)\n",
    "    elif pd.isna(glod_real[i+1]):\n",
    "        #print(i,\" + 1 is non \")\n",
    "        # first clear today's money\n",
    "        process[i,0] = process[i-1,0]* bit_real[i]/bit_real[i-1]\n",
    "        process[i,1] = process[i-1,1] \n",
    "        process[i,2] = process[i-1,2]\n",
    "        tempi = 1\n",
    "        future_fac = 0\n",
    "        while pd.isna(glod_real[i+tempi]):\n",
    "            future_fac = future_fac+ (bit[i+tempi]-bit[i+tempi-1])/bit[i+tempi-1]\n",
    "            tempi=tempi+1\n",
    "        future_fac =  future_fac + (bit[i+tempi]-bit[i+tempi-1])/bit[i+tempi-1]\n",
    "        future_gold =(1+ (gold[i+tempi] - glod_real[i])/ glod_real[i] - future_fac ) * glod_real[i+tempi]\n",
    "        #future_gold = gold[i+tempi]\n",
    "        process_temp = lingo_solve1(future_gold,bit[i+1],glod_real[i],bit_real[i],process[i,0],process[i,1],process[i,2])\n",
    "        #print( future_gold )\n",
    "        process[i,0] = process_temp[0]\n",
    "        process[i,1] = process_temp[1]\n",
    "        process[i,2] = process_temp[2]\n",
    "    else:\n",
    "        tempi = 1\n",
    "        while(pd.isna(glod_real[i-tempi])):\n",
    "            tempi=tempi+1\n",
    "        history_gold = glod_real[i-tempi] # 找出历史上次的金价\n",
    "        process[i,0] = process[i-1,0] * bit_real[i]/bit_real[i-1]\n",
    "        process[i,1] = process[i-1,1] * glod_real[i]/history_gold\n",
    "        process[i,2] = process[i-1,2]\n",
    "        # solve\n",
    "        process_temp = lingo_solve1(gold[i+1],bit[i+1],glod_real[i],bit_real[i],process[i-1,0],process[i-1,1],process[i-1,2])\n",
    "        process[i,0] = process_temp[0]\n",
    "        process[i,1] = process_temp[1]\n",
    "        process[i,2] = process_temp[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1193.71535748015,\n",
       " 756.055967368625,\n",
       " 1205.05,\n",
       " 780.92,\n",
       " 1.4020620684384726e-05,\n",
       " 580.016505966799,\n",
       " 247.86976362822884)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold[i+1],bit[i+1],glod_real[i],bit_real[i],process[i-1,0],process[i-1,1],process[i-1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    " res = lingo_solve1(gold[i+1],bit[i+1],glod_real[i],bit_real[i],process[i-1,0],process[i-1,1],process[i-1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "     con: array([688.59419242])\n",
      "     fun: -128.42186002265703\n",
      " message: 'The algorithm terminated successfully and determined that the problem is infeasible.'\n",
      "     nit: 5\n",
      "   slack: array([  -8.06223736, -696.35188385,  620.17574376,  532.40757304])\n",
      "  status: 2\n",
      " success: False\n",
      "       x: array([ 0.73896895, 88.50713967, 40.22318479,  4.52649832,  0.3635739 ])\n"
     ]
    }
   ],
   "source": [
    "print(res[0])\n",
    "print(res[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Stock.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "      <th>predict</th>\n",
       "      <th>USD (PM)</th>\n",
       "      <th>gold_pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9/11/16</td>\n",
       "      <td>621.65</td>\n",
       "      <td>621.650000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9/12/16</td>\n",
       "      <td>609.67</td>\n",
       "      <td>609.670000</td>\n",
       "      <td>1324.60</td>\n",
       "      <td>1324.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9/13/16</td>\n",
       "      <td>610.92</td>\n",
       "      <td>610.920000</td>\n",
       "      <td>1323.65</td>\n",
       "      <td>1323.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9/14/16</td>\n",
       "      <td>608.82</td>\n",
       "      <td>608.820000</td>\n",
       "      <td>1321.75</td>\n",
       "      <td>1321.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9/15/16</td>\n",
       "      <td>610.38</td>\n",
       "      <td>610.380000</td>\n",
       "      <td>1310.80</td>\n",
       "      <td>1310.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>1821</td>\n",
       "      <td>9/6/21</td>\n",
       "      <td>51769.06</td>\n",
       "      <td>50050.244717</td>\n",
       "      <td>1821.60</td>\n",
       "      <td>1818.095970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>1822</td>\n",
       "      <td>9/7/21</td>\n",
       "      <td>52677.40</td>\n",
       "      <td>51675.820108</td>\n",
       "      <td>1802.15</td>\n",
       "      <td>1804.708288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>1823</td>\n",
       "      <td>9/8/21</td>\n",
       "      <td>46809.17</td>\n",
       "      <td>52794.792401</td>\n",
       "      <td>1786.00</td>\n",
       "      <td>1789.537063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>1824</td>\n",
       "      <td>9/9/21</td>\n",
       "      <td>46078.38</td>\n",
       "      <td>47399.338958</td>\n",
       "      <td>1788.25</td>\n",
       "      <td>1788.065585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>1825</td>\n",
       "      <td>9/10/21</td>\n",
       "      <td>46368.69</td>\n",
       "      <td>45780.351748</td>\n",
       "      <td>1794.60</td>\n",
       "      <td>1791.279405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1826 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0     Date     Value       predict  USD (PM)     gold_pre\n",
       "0              0  9/11/16    621.65    621.650000       NaN          NaN\n",
       "1              1  9/12/16    609.67    609.670000   1324.60  1324.600000\n",
       "2              2  9/13/16    610.92    610.920000   1323.65  1323.650000\n",
       "3              3  9/14/16    608.82    608.820000   1321.75  1321.750000\n",
       "4              4  9/15/16    610.38    610.380000   1310.80  1310.800000\n",
       "...          ...      ...       ...           ...       ...          ...\n",
       "1821        1821   9/6/21  51769.06  50050.244717   1821.60  1818.095970\n",
       "1822        1822   9/7/21  52677.40  51675.820108   1802.15  1804.708288\n",
       "1823        1823   9/8/21  46809.17  52794.792401   1786.00  1789.537063\n",
       "1824        1824   9/9/21  46078.38  47399.338958   1788.25  1788.065585\n",
       "1825        1825  9/10/21  46368.69  45780.351748   1794.60  1791.279405\n",
       "\n",
       "[1826 rows x 6 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stock\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.plot(process[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.plot(process[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [0.00000000e+00, 6.43659257e+04, 2.37291381e-10],\n",
       "       [0.00000000e+00, 6.47714537e+04, 1.45577905e-10],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "process_g = process[:,0]\n",
    "diff1 = np.diff(process_g)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e7148e05e0>]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sstock "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
